opensense april 2012 v1

8/2/2019 Opensense April 2012 v1

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OpenSense

OPENSENSE

OPEN INFRASTRUCTUREFOR AIR QUALITY MONITORING

Karl Aberer, EPFL

Boi Faltings, Alcherio Martinoli, Martin Vetterli, EPFL

Lothar Thiele, ETH ZrichJan Blom, Nokia Research Center Lausanne


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OVERVIEW

Motivation and Research challenges

Research progress and results

1. Sensing System

2. Data Analysis3. User Concerns

4. End-to-end System Architecture

Conclusions


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MOTIVATION AND RESEARCH CHALLENGES


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AIR POLLUTION

Air pollution in urban areas is a global concern

affects quality of life and health

urban population is increasing

Deaths from Urban Air Pollution (courtesy CHUV)


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CITIZEN USE: NOKIA USER STUDYKey questions:

How is air quality perceivedand how does it affect thedaily life?

Method:

Probe study including diaryabout the different air qualitysituations in the familys life

Participants:Six families from

Helsinki region


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EXAMPLE FINDINGS

Understanding the limitationsof senses: what pollutants wecannot sense?

The lack of air pollution related knowledge and perceptual gaps

What are thehealth impactsof differentpollutants?

What are preventivestrategies?

Connection betweenperception and objectivetruth?

For instance, is the windcoming from seas

direction really fresh?

What pollutants areincluded in the perceptionof polluted air?

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DESIGN DRIVERS

Track Back Air

Care Through Air

Air Belongs to All

Relieve and DiscoverFresh Moments

My Mobile Air

Edutain Air


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Care Through Air

Parents taking care of their childrenwith the help of an air quality service

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AIR POLLUTIONAND

CARDIOVASCULAR MORTALITYHealth studies show that air pollution increases the risk of cardiovascular mortality(hearth attacks) by 5% to 20% at least

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HEALTH STUDY SCENARIO

Under discussion with University Hospital CHUV and NOKIA

Assessment of impact of air pollution an health

E.g. blood pressure, renal activity, respiration

Effects are immediate High temporal and spatial

resolution of air quality datarequired

Trajectories of study subjectsare needed

Correlation with activity

and health parameters On-body sensors Activity recognition (using

mobile phones)

User concerns Study participants are sensitive

data privacy Participants would like

personalized information aboutindividual exposure and risks

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CHALLENGESTOMEET

1. Sensing system With sufficient temporal and spatial resolution

With sufficient precision

At reasonable cost

2. Data analysis Interpolate air quality parameters from raw data

Ensure data quality

Reduce acquisition cost

3. User concerns Correlate with activity and mobility data Consider privacy concerns

Provide individualized information

4. End-to-end system architecture

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1. S ENSING SYSTEM

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MONITORING TODAY

Stationary andexpensive stations

Expensive mobilehigh fidelity equipment

Sparse sensor network(Nabel)

Coarse models

(mesoscale = 1km2)

Data difficult to integrate into applications(e.g. for correlating with other features like

peoples activities)

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OPPORTUNITIES

Wireless communication

and low cost sensors:deploy larger numbers ofstations

Mobility:deploy mobile stations toincrease spatial coverage

Communities:citizens as data producersand informationconsumers

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SENSOR MODELING Understand behavior of

electro-chemical sensors

sensor dynamics

linearity

sensitivity to humidity

variability with different flowconditions

Output[V]

Selected City Technology A3CO Sensor - measured and modeled response

Martinoli

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SAMPLING SYSTEM Slow response of chemical

sensors replacing passive sampling with

active sniffing 3D printing for fast prototyping 1st model of sniffer for CO2 sensor

faster response by more than 50%

inlet

outlet

towards miniature pump

Telaire 6613 CO2 sensor

Prototype CO2 snifferon Khepera III mobile robot

Amplitude[ppm]

Martinoli

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ON-THE-FLY CALIBRATION Challenge:

Supplied calibration may not match project requirements

Baseline drift due to sensor aging

Approach:

Initial calibration using stationary, high quality instruments

When deployed periodic recalibration using mobile sensor nodesOriginal calibrationperforms with anaverage error of

30ppb

After recalibrationthe average errordrops below 3ppb

Thiele D. Hasenfratz, O. Saukh, and L. Thiele, On-the-fly Calibration of Low-cost GasSensors, Proc. of 9th European Conference on Wireless Sensor Networks (EWSN2012), Trento, Italy, Feb. 2012.

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MULTI-HOP CALIBRATION

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PLATFORMS

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LAUSANNE DEPLOYMENT

Planned12 stationary stations

NO2, CO, Humidity,Temperature

Solar panel powered Communication: GSM,

Wireless multi-hop routing

8 mobile stations NO2, CO, CO2, Humidity, Temperature

Positioning module Powered by bus Communication: GSM

1 prototype station mounted on bus

Vetterli

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LAUSANNE COVERAGE

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DATAFROM LAUSANNE DEPLOYMENT

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ZRICHMOBILE

5 stations mounted on Trams O3, CO, fine particles

Temperature, humidity, acceleration

Communication: GSM, WLAN

Calibration Under lab conditions at EMPA

On-the-fly using 3 reference stations

in the cityPlan: 10 stations

Thiele

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HARDWARE

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INSTALLATION @ TRAM 3005

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ZRICH DATA

CO concentration PM concentration

Pollutant # of Measurements Sampling rate Time Period

Particulate matter 116000 5s 2 months

Ozone 350000 20s 5 months

CO 350000 20s 5 months

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PERSONAL MOBILE SENSOR

ThieleD. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele. Participatory Air Pollution

Monitoring Using Smartphones. In the 1st International Workshop on Mobile

Sensing: From Smartphones and Wearables to Big Data, Beijing, China, 2012.

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GSN @ DATA.OPENSENSE.ETHZ.CH

Thiele, Aberer et al

Thiele Aberer

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2. DATA ANALYSIS

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OPTIMAL SENSINGFOR MOVING SENSORS

Goal: find an optimal sensing strategy, which provides an appropriate balancebetween maximize sensing coverage ofmoving sensors and minimizesensing cost (sampling)?

Question: Can segmentation help?

Aberer Z. Yan, J. Eberle, K. Aberer, OptiMoS: Optimal Sensing for Mobile Sensors, 13thInternational Conference on Mobile Data Management (MDM). Bengaluru, India,July 2012

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RESULTS FOR LAUSANNE DATA Segmentation

Evaluated heuristicsegmentation strategies

Compared to optimal

On existing datasets about 5segments sufficient

Sampling

Evaluated heuristic strategies

Entropy-based strategyperforms best

Current sampling rate about 5times too high

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REGION-BASED MODEL Observation

pollution tends to be relativelyhomogeneous within regions

Approach Tracking trends of pollution level in

regions (streets, residential blocks,

parks) Annotate regions with relevant

emission, land use, meteorology andmeasurement data.

Goals learning structured causal relations

from past data Interpolate real time measurement Understand possible causes of

pollution levels

FaltingsJ. J. Li, B. V. Faltings, Towards a Qualitative, Region-Based Model for Air Pollution

Dispersion, IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI),

2011.Thiele

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NON-STATIONARY GAUSSIAN MODEL Interpolate with a global non-

stationary spatial model based onGaussian Process regression

Learning spatial covariance frompast data

Validate model with Strasbourgsimulations

A fully modular java toolkit formodeling/visualizing pollution.

J. J. Li, B. Faltings, O. Saukh, D. Hasenfratz, and J. Beutel. Sensing the Air We Breathe theOpenSense Dataset. In the Proceedings of the 26th International Conference on ArtificialIntelligence (AAAI), Toronto, Canada, 2012.

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3. USER CONCERNS

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SEMANTIC INDOOR ACTIVITIES:

LEARNINGFROM ACCELEROMETER

Dataset

Nokia N95

6 users

1-2 months daily indoor activities

User activity tag cloud

Aberer

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TWO-TIER SEMANTIC ACTIVITY LEARNING

Robust feature set for Micro Activity inference sit, stand, walk, sitActive, loiter etc.

Set/Sequential features for Semantic Activity inference Home activities: cook, work, relax, break, eat, baby-care, etc.

Office activities: work, lunch, break, meeting, toilet, coffee, etc.

Raw

Accelerometer

Stream

a1

a2

an

an+1

an+2

an+m

GPS Location: office GPS Location: home

Layer I. Micro-Ac vity Inference [MA](Classifica on using Frame-Level Raw Accelerometer Features)

Layer II. High-level (Seman c) Ac vity Inference [HA](Discrimina ve Set & Sequen al Pa ern Features on MA streams)

HA1=break@office HA2=cooking@home

Inferred MA

Streams

Inferred HA

Labels

Frame Frame Frame FrameFrame Frame Frame FrameFrame Frame

Z. Yan, D. Chakraborty, A. Misra, H. Jeung, K. Aberer, SAMMPLE: Detecting Semantic

Indoor Activities in Practical Settings using Locomotive Signatures, 16th International

Symposium on Wearable Computers (ISWC), Newcastle, UK, June 2012

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Participatory sensing

Users reveal location

Semi-honest aggregation serverinfers user activity

Obfuscation affects data quality

Approach

Personalized privacy

Users estimate potential privacy loss

USER PRIVACYVS. DATA RELIABILITY

Possiblemoves

Obfuscatedtrajectory

D(t)= dist(postrue(t),Y(t))P(Y, t)Y posobf(t)

Aberer B.Agir, T.Papaioannou, R.Narendula, K.Aberer, J.P. Hubaux.,An adaptive scheme for personalized privacy in participatorysensing, WiSec 2012.

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EXPERIMENTAL RESULTS

Real data for electrosmog sensing by Nokia campaign

Avg Static : static parameters that meet the threshold on the average

Max Static : static parameters that always meet the threshold

error completeness

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4. END-TO-END SYSTEM ARCHITECTURE

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CONTROL: WHATISTHEPROBLEM?

1. Node decides individually depending on its state, e.g. calibration

2. Nodes communicate with WSN and coordinate

3. Base station schedules nodes using mobility model: a third node arrives,dont measure!

4. Air quality model: dont need measurement!

5. Privacy model: node 1 should measure!6. Application model (e.g. health service):

no measurement needed!

Two mobile nodes:who should measure?

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Approach

Data aggregator produces amodel cover from a set ofmodels on an area

Continuous sensor updates Continuous and ad-hoc queries

Goal Consider privacy concerns Account for trustworthiness Optimize social utility

MULTI-MODEL QUERY PROCESSING

Continuous Moving Queries

Give a (in car) pollution updateevery 30 mins

Aggregate Queries

COX emitted yesterday inLausanne center

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Sensors

2D region

Randomly moving sensors

Inherent sensor inaccuracy

Not completely trusted

Users

Ask point queries

Obfuscate their location bymultiple requests

Limited budget

Data aggregator

Collects queries

Selects sensors

Optimizes using a utility function

Strategies

Greedy: iteratively select bestsensor for each query

PerLocation: iteratively selectbest sensor for each location

Randomized: repeat PerLocation

for different location orderings

SIMULATION STUDY

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SENSOR SELECTIONHEURISTICS

0

10

20

30

40

50

60

70

80

90

Trustworthy Trustworthiness_Uniform_[0.5,1] Trustworthiness_Uniform_[0,1]

TotalUtility

Trustworthiness of Sensors

Total utility of different sensor selection algorithms for different

trust distributions of sensors

Optimal

Randomized

PerLocationOptimal

Greedy

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SOCIAL UTILITY

0

5000

10000

15000

20000

25000

30000

TotalUtility

Privacy Sensitivity-Energy Cost Function

Total utility by the greedy algorithm for different sensitivity toprivacy, different trust distributions and energy cost functions

Trustworthy

Trustworthiness_Uniform_[0.5,1]

Trustworthiness_Uniform_[0,1]

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OPENSENSE ARCHITECTURE

cleanedcalibrated

data

Mobile sensors

Data

aggregation server

measurements,location, status

Environment modelsinterpolation/segment

ation

Applications

response

Data Flow

schedule(measurements, priority)

Schedulingcomponent

local coordination

sampling for locationsconsidering error, value

Service market

charges data costsubmits offers

checks data offerssubmits requests

queries

Data market

required samplespriority

Control Flow

landuse data

Mobility model sensor locations

predictions

Calibration modelCleaning model

Sensor model(e.g sensor wear)

Map dataLanduse data

sensor status

predictions

raw data

calibrated data

Context

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CONCLUSIONS

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COLLABORATIONS

e2V,SensorScope

, EMPA,FHNW

Sensing platformVetterli,

Martinoli,Thiele

TL, VBZ,PSA

Mobility platform Vetterli,Martinoli,

Thiele

Nokia,CHUV,

Swiss TPHApplications

Vetterli,Faltings,Aberer,Blom

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CONCLUSION

End-to-end system view crucial Investigate all system layers: sensor user interfaces

Utility-based framework as integrative approach

Availability of real data and user requirements crucial Last year will focus in particular on integration

Having a concrete health related scenario in mind

Results applicable beyond air pollution Complex, distributed, participatory measurement

For more information: opensense.epfl.ch

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TEAM Karl Aberer, EPFL-LSIR, project

leader

Thanasis Papaioannou, postdoc

Rammohan Narendula, PhD

Mehdi Riahi, PhD

Zhixian Yan, PhD Sofiane Sarni, engineer

Saket Sathe, PhD

Boi Faltings, EPFL-LIA, PI

Jason Jingshi Li, postdoc

Martin Vetterli, EPFL-LCAV, PI

Guillermo Barrenetxea, postdoc

Andrea Ridolfi, postdoc

Alcherio Martinoli, EPFL-DISAL, PI Chris Evans, PhD

Emanuel Droz, engineer

Adrian Arfire, PhD

Lothar Thiele, ETH Zrich, PI

Olga Saukh, postdoc Jan Beutel, postdoc

David Hasenfratz (PhD)

Christoph Walser (engineer)

opensense april 2012 v1

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